The statements in the background of the invention are provided to assist with understanding the invention and its applications and uses, and may not constitute prior art.
There are several approaches that have been tried to extract 3D measurements from images of 3D objects, including utilizing specialized 3D cameras as well as utilizing 2D videos or 2D photos, followed by 2D-to-3D reconstruction techniques to estimate 3D measurements.
A promising new technique described in U.S. Pat. No. 10,321,728 is to utilize deep learning networks to extract measurements from 2D photos taken using a single mobile device camera. This technique minimizes user frictions while promising to deliver highly accurate measurements. However, this approach may require large amounts of training data, including segmented and annotated front and side images, along with corresponding ground truth data, in order to train the deep learning networks. In practice, acquiring the large training data sets necessary for training deep learning networks is exceedingly difficult. Images have to be acquired from willing volunteers, segmented and annotated by human annotators, and matched with collected ground truth data of actual measurements.
Therefore, it would be an advancement in the state of the art to provide a process by which small data sets may be usefully amplified for training deep learning networks for measurements extraction. In this way, a small amount of data collected may be usefully amplified to create large data sets that may be used to train the deep learning algorithms for accurate measurements.
It is against this background that the present invention was developed.